Recently, some neural task-oriented dialogue agents that query underlying knowledge bases (KBs) and extract relevant entities either do the following: 1) create and execute well-formatted API calls to the KB, operations which require intermediate supervision in the form of training slot trackers and which break differentiability  [1]}, or 2) softly attend to the KB and combine this probability distribution with belief trackers as state input for a reinforcement learning policy  [2]}. We choose to build off the latter approach as it fits nicely into the end-to-end trainable framework of sequence-to-sequence modelling, though we are in a supervised learning setting and we do away with explicit representations of belief trackers or dialogue state.
